Abstract
Abstract. Nowadays many seasonal forecasting centres provide dynamical predictions of sea ice. While initializing sea ice by assimilating sea ice concentration (SIC) is common, constraining initial conditions of sea ice thickness (SIT) is only in its early stages. Here, we make use of the availability of Arctic-wide winter SIT observations covering 2011–2016 to constrain SIT in the ECMWF (European Centre for Medium-Range Weather Forecasts) ocean–sea-ice analysis system with the aim of improving the initial conditions of the coupled forecasts. The impact of the improved initialization on the predictive skill of pan-Arctic sea ice for lead times of up to 7 months is investigated in a low-resolution analogue of the currently operational ECMWF seasonal forecasting system SEAS5. By using winter SIT information merged from CS2 and SMOS (CS2SMOS: CryoSat-2 Soil Moisture and Ocean Salinity), substantial changes in sea ice volume and thickness are found in the ocean–sea-ice analysis, including damping of the overly strong seasonal cycle of sea ice volume. Compared with the reference experiment, which does not use SIT information, forecasts initialized using SIT data show a reduction of the excess sea ice bias and an overall reduction of seasonal sea ice area forecast errors of up to 5 % at lead months 2 to 5. Change in biases is the main forecast impact. Using the integrated ice edge error (IIEE) metric, we find significant improvement of up to 28 % in the September sea ice edge forecast started in April. However, sea ice forecasts for September started in spring still exhibit a positive sea ice bias, which points to a melting that is too slow in the forecast model. A slight degradation in skill is found in the early freezing season sea ice forecasts initialized in July and August, which is related to degraded initial conditions during these months. Both ocean reanalyses, with and without SIT constraint, show strong melting in the middle of the melt season compared to the forecasts. This excessive melting related to positive net surface radiation biases in the atmospheric flux forcing of the ocean reanalyses remains and consequently degrades analysed summer SIC. The impact of thickness initialization is also visible in the sea surface and near-surface temperature forecasts. While positive forecast impact is seen in near-surface temperature forecasts of early freezing season (September–October–November) initialized in May (when the sea ice initial conditions have been observationally constrained in the preceding winter months), negative impact is seen for the same season when initialized in the month of August when the sea ice initial conditions are degraded. We conclude that the strong thinning by CS2SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea ice and near-surface temperatures in all seasons. The results indicate that the memory of SIT in the spring initial conditions lasts into autumn, influencing forecasts of the peak summer melt and early freezing seasons. Our results demonstrate the usefulness of new sea ice observational products in both data assimilation and forecasting systems, and they strongly suggest that better initialization of SIT is crucial for improving seasonal sea ice forecasts.
Highlights
Sea ice is an integral part of the Earth system as it regulates the heat, moisture and momentum flux exchange between the polar oceans and the atmosphere
Each of the experiments consists of two distinctive steps: (1) the production of a set of ocean and sea ice initial conditions by conducting twin ocean–sea-ice assimilation experiments, which only differ in the use of sea ice thickness (SIT) information; and (2) the production of a set of twin retrospective seasonal forecast experiments, initialized from the respective ORA
We first assess the impact of sea ice thickness observations on the estimation of sea ice properties in the ORA initial conditions, and we evaluate the impact on the skill of seasonal forecast of sea ice area, sea ice edge, sea ice volume and 2 m temperature
Summary
Sea ice is an integral part of the Earth system as it regulates the heat, moisture and momentum flux exchange between the polar oceans and the atmosphere. Decline in Arctic sea ice is a visible indicator of the changing climate. Forecasting Arctic sea ice has advanced significantly in the last decade, with most forecasting centres using prognostic sea ice models operationally, allowing us to explore the sea ice forecast skill on long lead times from weeks to months to seasons. And seasonal outlooks of sea ice products are in great demand especially by the Arctic communities, as well as maritime and resource extraction industries. There is increasing scientific evidence that warming and sea ice loss in the Arctic due to climate change affect the European weather and climate (Balmaseda et al, 2010; Mori et al, 2014; Overland et al, 2016; Ruggieri et al, 2016). Unlike sea ice concentration and extent, long records of satellite observations of sea ice thickness are sorely lacking (Laxon et al, 2003; Kwok and Rothrock, 2009; Haas et al, 2010; Meier et al, 2014; Sallila et al, 2019; Scarlat et al, 2020)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
